In wireless multi-hop networks such as ad hoc networks and sensor networks, backoff-based opportunistic routing protocols, which make a forwarding decision based on backoff time, have been proposed. In the protocols, each potential forwarder calculates the backoff time based on the product of a weight and global scaling factor. The weight prioritizes potential forwarders and is calculated based on hop counts to the destination of a sender and receiver. The global scaling factor is a predetermined value to map the weight to the actual backoff time. However, there are three common issues derived from the global scaling factor. First, it is necessary to share the predetermined global scaling factor with a centralized manner among all terminals properly for the backoff time calculation. Second, it is almost impossible to change the global scaling factor during the networks are being used. Third, it is difficult to set the global scaling factor to an appropriate value since the value differs among each local surrounding of forwarders. To address the aforementioned issues, this paper proposes a novel decentralized local scaling factor control without relying on a predetermined global scaling factor. The proposed method consists of the following three mechanisms: (1) sender-centric local scaling factor setting mechanism in a decentralized manner instead of the global scaling factor, (2) adaptive scaling factor control mechanism which adapts the local scaling factor to each local surrounding of forwarders, and (3) mitigation mechanism for excessive local scaling factor increases for the local scaling factor convergence. Finally, this paper evaluates the backoff-based opportunistic routing protocol with and without the proposed method using computer simulations.

In this letter, we propose a novel wireless power transfer (WPT) scheme in the radiative near-field (Fresnel) region, which based on machine vision and dynamically reconfigurable holographic metasurface aperture capable of focusing power to multiple spots simultaneously without any information feedback. The states of metamaterial elements, formed by tunable meander line resonators, is determined using holographic design principles, in which the interference pattern of reference mode and the desired radiated field pattern leads to the required phase distribution over the surface of the aperture. The three-dimensional position information of mobile point sources is determined by machine visual localization, which can be used to obtain the aperture field. In contrast to the existing research studies, the proposed scheme is not only designed to achieve free multi-focuses, but also with machine vision, low-dimensionality, high transmission efficiency, real-time continuous reconfigurability and so on. The accuracy of the analysis is confirmed using numerical simulation.

This paper proposes and examines the three in-memory shuffling methods designed to address problems in MapReduce shuffling caused by skewed data. Coupled Shuffle Architecture (CSA) employs a single pairwise all-to-all exchange to shuffle both blocks, units of shuffle transfer, and meta-blocks, which contain the metadata of corresponding blocks. Decoupled Shuffle Architecture (DSA) separates the shuffling of meta-blocks and blocks, and applies different all-to-all exchange algorithms to each shuffling process, attempting to mitigate the impact of stragglers in strongly skewed distributions. Decoupled Shuffle Architecture with Skew-Aware Meta-Shuffle (DSA w/ SMS) autonomously determines the proper placement of blocks based on the memory consumption of each worker process. This approach targets extremely skewed situations where some worker processes could exceed their node memory limitation. This study evaluates implementations of the three shuffling methods in our prototype in-memory MapReduce engine, which employs high performance interconnects such as InfiniBand and Intel Omni-Path. Our results suggest that DSA w/ SMS is the only viable solution for extremely skewed data distributions. We also present a detailed investigation of the performance of CSA and DSA in various skew situations.

Recently, many studies have applied gamification to software engineering education and software development to enhance work results. Gamification is defined as “the use of game design elements in non-game contexts.” When applying gamification, we make various game rules, such as a time limit. However, it is not clear whether the rule affects working time or not. For example, if we apply a time limit to impatient developers, the working time may become shorter, but the rule may negatively affect because of pressure for time. In this study, we analyze with subjective experiments whether the rules affects work results such as working time. Our experimental results suggest that for the coding tasks, working time was shortened when we applied a rule that made developers aware of working time by showing elapsed time.

The monocone antenna is a type of monopole antenna that has wideband characteristics. This paper proposes a low-profile monocone antenna with a planar inverted-F structure. The characteristics of the proposed antenna are analyzed through a simulation. The results demonstrate that the low-profile antenna offers wideband performance, and the relative bandwidth of VSWR ≤ 2 is found to be more than 190%. In addition, miniaturization of the monocone antenna is elucidated. The proposed antenna is prototyped, and the validity of the simulation is verified through measurements.

A travel route recommendation service that recommends a sequence of points of interest for tourists traveling in an unfamiliar city is a very useful tool in the field of location-based social networks. Although there are many web services and mobile applications that can help tourists to plan their trips by providing information about sightseeing attractions, travel route recommendation services are still not widely applied. One reason could be that most of the previous studies that addressed this task were based on the orienteering problem model, which mainly focuses on the estimation of a user-location relation (for example, a user preference). This assumes that a user receives a reward by visiting a point of interest and the travel route is recommended by maximizing the total rewards from visiting those locations. However, a location-location relation, which we introduce as a transition pattern in this paper, implies useful information such as visiting order and can help to improve the quality of travel route recommendations. To this end, we propose a travel route recommendation method by combining location and transition knowledge, which assigns rewards for both locations and transitions.

Detecting community structures and analyzing temporal evolution in dynamic networks are challenging tasks to explore the inherent characteristics of the complex networks. In this paper, we propose a semi-supervised evolutionary clustering model based on symmetric nonnegative matrix factorization to detect communities in dynamic networks, named sEC-SNMF. We use the results of community partition at the previous time step as the priori information to modify the current network topology, then smooth-out the evolution of the communities and reduce the impact of noise. Furthermore, we introduce a community transition probability matrix to track and analyze the temporal evolutions. Different from previous algorithms, our approach does not need to know the number of communities in advance and can deal with the situation in which the number of communities and nodes varies over time. Extensive experiments on synthetic datasets demonstrate that the proposed method is competitive and has a superior performance.

In integrated circuit design of advanced technology nodes, layout density uniformity significantly influences the manufacturability due to the CMP variability. In analog design, especially, designers are suffering from passing the density checking since there are few useful tools. To tackle this issue, we focus a transistor-array(TA)-style analog layout, and propose a density optimization algorithm consistent with complicated design rules. Based on TA-style, we introduce a density-aware layout format to explicitly control the layout pattern density, and provide the mathematical optimization approach. Hence, a design flow incorporating our density optimization can drastically reduce the design time with fewer iterations. In a design case of an OPAMP layout in a 65nm CMOS process, the result demonstrates that the proposed approach achieves more than 48× speed-up compared with conventional manual layout, meanwhile it shows a good circuit performance in the post-layout simulation.

This paper addresses the problem of developing an efficient fault-tolerant routing method for 2D mesh Network-on-Chips (NoCs) to realize dependable and high performance many core systems. Existing fault-tolerant routing methods have two critical problems of high communication latency and low node utilization. Unlike almost all existing methods where packets always detour faulty nodes, we propose a novel and unique approach that packets can pass through faulty nodes. For this approach, we enhance the common NoC architecture by adding switches and links around each node and propose a fault-tolerant routing method with no virtual channels based on the well-known simple XY routing method. Simulation results show that the proposed method reduces average communication latency by about 97.1% compared with the existing method, without sacrificing fault-free nodes.

In this paper we design a fast fabric defect detection framework (Fast-DDF) based on gray histogram back-projection, which adopts end to end multi-convoluted network model to realize defect classification. First, the back-projection image is established through the gray histogram on fabric image, and the closing operation and adaptive threshold segmentation method are performed to screen the impurity information and extract the defect regions. Then, the defect images segmented by the Fast-DDF are marked and normalized into the multi-layer convolutional neural network for training. Finally, in order to solve the problem of difficult adjustment of network model parameters and long training time, some strategies such as batch normalization of samples and network fine tuning are proposed. The experimental results on the TILDA database show that our method can deal with various defect types of textile fabrics. The average detection accuracy with a higher rate of 96.12% in the database of five different defects, and the single image detection speed only needs 0.72s.

The least mean squares (LMS) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of O(2N) where N is the number of taps. The drawback of the LMS algorithm is that its performance is sensitive to the scaling of the input. The normalized LMS (NLMS) algorithm solves this problem on the LMS algorithm by normalizing with the sliding-window power of the input; however, this normalization increases the computational cost to O(3N) per iteration. In this work, we derive a new formula to strictly perform the NLMS algorithm at a computational complexity of O(2N), that is referred to as the C-NLMS algorithm. The derivation of the C-NLMS algorithm uses the H∞ framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-NLMS algorithm is verified using simulations.

Sparse arrays can usually achieve larger array apertures than uniform linear arrays (ULA) with the same number of physical antennas. However, the conventional direction-of-arrival (DOA) estimation algorithms for sparse arrays usually require the spatial smoothing operation to recover the matrix rank which inevitably involves heavy computational complexity and leads to a reduction in the degrees-of-freedom (DOFs). In this paper, a low-complex DOA estimation algorithm by exploiting the discrete Fourier transform (DFT) is proposed. Firstly, the spatial spectrum of the virtual array constructed from the sparse array is established by exploiting the DFT operation. The initial DOA estimation can obtain directly by searching the peaks in the DFT spectrum. However, since the number of array antennas is finite, there exists spectrum power leakage which will cause the performance degradation. To further improve the angle resolution, an iterative process is developed to suppress the spectrum power leakage. Thus, the proposed algorithm does not require the spatial smoothing operation and the computational complexity is reduced effectively. In addition, due to the extention of DOF with the application of the sparse arrays, the proposed algorithm can resolve the underdetermined DOA estimation problems. The superiority of the proposed algorithm is demonstrated by simulation results.

The estimation of the matrix rank of harmonic components of a music spectrogram provides some useful information, e.g., the determination of the number of basis vectors of the matrix-factorization-based algorithms, which is required for the automatic music transcription or in post-processing. In this work, we develop an algorithm based on Stein's unbiased risk estimator (SURE) algorithm with the matrix factorization model. The noise variance required for the SURE algorithm is estimated by suppressing the harmonic component via median filtering. An evaluation performed using the MIDI-aligned piano sounds (MAPS) database revealed an average estimation error of -0.26 (standard deviation: 4.4) for the proposed algorithm.

Recently, more and more people start investing. Understanding the factors affecting financial products is important for making investment decisions. However, it is difficult to understand factors for novices because various factors affect each other. Various technique has been studied, but conventional factor analysis methods focus on revealing the impact of factors over a certain period locally, and it is not easy to predict net asset values. As a reasonable solution for the prediction of net asset values, in this paper, we propose a trend shift model for the global analysis of factors by introducing trend change points as shift interference variables into state space models. In addition, to realize the trend shift model efficiently, we propose an effective trend detection method, TP-TBSM (two-phase TBSM), by extending TBSM (trend-based segmentation method). Comparing with TBSM, TP-TBSM could detect trends flexibly by reducing the dependence on parameters. We conduct experiments with eleven investment trust products and reveal the usefulness and effectiveness of the proposed model and method.

Although correlation filter-based trackers have demonstrated excellent performance for visual object tracking, there remain several challenges to be addressed. In this work, we propose a novel tracker based on the correlation filter framework. Traditional trackers face difficulty in accurately adapting to changes in the scale of the target when the target moves quickly. To address this, we suggest a scale adaptive scheme based on prediction scales. We also incorporate a speed-based adaptive model update method to further improve overall tracking performance. Experiments with samples from the OTB100 and KITTI datasets demonstrate that our method outperforms existing state-of-the-art tracking algorithms in fast motion scenes.

We numerically investigate that optimal robust onion-like networks can emerge even with the constraint of surface growth in supposing a spatially embedded transportation or communication system. To be onion-like, moderately long links are necessary in the attachment through intermediations inspired from a social organization theory.

Unmanned aerial vehicle mounted base stations (UAV-BSs) can provide wireless cellular service to ground users in a variety of scenarios. The efficient deployment of such UAV-BSs while optimizing the coverage area is one of the key challenges. We investigate the deployment of UAV-BS to maximize the coverage of ground users, and further analyzes the impact of the deployment of UAV-BS on the fairness of ground users. In this paper, we first calculated the location of the UAV-BS according to the QoS requirements of the ground users, and then the fairness of ground users is taken into account by calculating three different fairness indexes. The performance of two genetic algorithms, namely Standard Genetic Algorithm (SGA) and Multi-Population Genetic Algorithm (MPGA) are compared to solve the optimization problem of UAV-BS deployment. The simulations are presented showing that the performance of the two algorithms, and the fairness performance of the ground users is also given.

In this paper, a new transceiver system for the in-vehicle communication system is proposed to enhance data transmission rate and timing accuracy in TDM-based application. The proposed system utilizes point-to-point (P2P) channel, a closed-loop clock forwarding path, and a transceiver with a repeater and clock delay adjuster. The proposed system with 4 ECU (Electronic Computing Unit) nodes is implemented in 180nm CMOS technology and, when compared with conventional bus-based system, achieved more than 125 times faster data transmission. The maximum data rate was 2.5Gbps at 1.8V power supply and the worst peak-to-peak jitter for the data and clock signals over 5000 data symbols were about 49.6ps and 9.8ps respectively.

Energy consumption is one of the important issues in communication networks, and it is expected that network devices such as network interface cards will be turned off to decrease the energy consumption. Moreover, fast failure recovery is an important issue in large-scale communication networks to minimize the impact of failure on data transmission. In order to realize both low energy consumption and fast failure recovery, a method called LE-MRC (Low-Energy based Multiple Routing Configurations) has been proposed. However, LE-MRC can degrade network robustness because some links ports are turned off for reducing the energy consumption. Nevertheless, network robustness is also important for maintaining the performance of data transmission and the network functionality. In this paper, for realizing both low energy consumption and fast failure recovery while maintaining network robustness, we propose Robustness and Low-Energy based Multiple Routing Configurations (RLE-MRC). In RLE-MRC, some links are categorized into unnecessary links, and those links are turned off to lower the energy consumption. In particular, the number of excluded links is determined based on the network robustness. As a result, the energy consumption can be reduced so as not to degrade the network robustness significantly. Simulations are conducted on some network topologies to evaluate the performance of RLE-MRC. We also use ns-3 to evaluate how the performance of data transmission and network robustness are changed by using RLE-MRC. Numerical examples show that the low energy consumption and the fast failure recovery can be achieved while maintaining network robustness by using RLE-MRC.

The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.